Relevant bibliographies by topics / Dynamic change models

Academic literature on the topic 'Dynamic change models'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Dynamic change models"

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Kramer, Walter, Werner Ploberger, and Raimund Alt. "Testing for Structural Change in Dynamic Models." Econometrica 56, no. 6 (November 1988): 1355. http://dx.doi.org/10.2307/1913102.

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Kim, Yongku, and L. Mark Berliner. "Change of spatiotemporal scale in dynamic models." Computational Statistics & Data Analysis 101 (September 2016): 80–92. http://dx.doi.org/10.1016/j.csda.2016.02.013.

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Zeileis, Achim, Friedrich Leisch, Christian Kleiber, and Kurt Hornik. "Monitoring structural change in dynamic econometric models." Journal of Applied Econometrics 20, no. 1 (January 2005): 99–121. http://dx.doi.org/10.1002/jae.776.

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Medina, Esperanza G. Valdés y., and Leilani Medina Valdés. "Dynamic Models as Change Enablers in Educational Mathematics." Procedia - Social and Behavioral Sciences 176 (February 2015): 923–26. http://dx.doi.org/10.1016/j.sbspro.2015.01.559.

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Falkenström, Fredrik, Steven Finkel, Rolf Sandell, Julian A. Rubel, and Rolf Holmqvist. "Dynamic models of individual change in psychotherapy process research." Journal of Consulting and Clinical Psychology 85, no. 6 (2017): 537–49. http://dx.doi.org/10.1037/ccp0000203.

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Han, Xu, and Atsushi Inoue. "TESTS FOR PARAMETER INSTABILITY IN DYNAMIC FACTOR MODELS." Econometric Theory 31, no. 5 (September 15, 2014): 1117–52. http://dx.doi.org/10.1017/s0266466614000486.

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In this paper, we develop tests for structural breaks of factor loadings in dynamic factor models. We focus on the joint null hypothesis that all factor loadings are constant over time. Because the number of factor loading parameters goes to infinity as the sample size grows, conventional tests cannot be used. Based on the fact that the presence of a structural change in factor loadings yields a structural change in second moments of factors obtained from the full sample principal component estimation, we reduce the infinite-dimensional problem into a finite-dimensional one and our statistic compares the pre- and postbreak subsample second moments of estimated factors. Our test is consistent under the alternative hypothesis in which a fraction of or all factor loadings have structural changes. The Monte Carlo results show that our test has good finite-sample size and power.
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Keith, David A., H. Resit Akçakaya, Wilfried Thuiller, Guy F. Midgley, Richard G. Pearson, Steven J. Phillips, Helen M. Regan, Miguel B. Araújo, and Tony G. Rebelo. "Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models." Biology Letters 4, no. 5 (July 29, 2008): 560–63. http://dx.doi.org/10.1098/rsbl.2008.0049.

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Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.
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Reuning, Kevin, Michael R. Kenwick, and Christopher J. Fariss. "Exploring the Dynamics of Latent Variable Models." Political Analysis 27, no. 4 (April 11, 2019): 503–17. http://dx.doi.org/10.1017/pan.2019.1.

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Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in the latent trait. Simulations demonstrate that the robust model outperforms other models when the underlying latent trait is subject to rapid change, and is equivalent to the dynamic model in the absence of volatility. We reproduce latent estimates from studies of judicial ideology and democracy. For judicial ideology, the robust model uncovers shocks in judicial voting patterns that were not previously identified in the dynamic model. For democracy, the robust model provides more precise estimates of sudden institutional changes such as the imposition of martial law in the Philippines (1972–1981) and the short-lived Saur Revolution in Afghanistan (1978). Overall, the robust model is a useful alternative to the standard dynamic model for modeling latent traits that change rapidly over time.
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de la Pena, Victor, and Ricardo Rivera. "Dynamic backtesting of value-at-risk models under regime change." Journal of Risk Model Validation 1, no. 4 (March 2008): 95–110. http://dx.doi.org/10.21314/jrmv.2008.013.

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Hilgert, Nadine, Ghislain Verdier, and Jean-Pierre Vila. "Change detection for uncertain autoregressive dynamic models through nonparametric estimation." Statistical Methodology 33 (December 2016): 96–113. http://dx.doi.org/10.1016/j.stamet.2016.08.003.

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Dissertations / Theses on the topic "Dynamic change models"

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Moreira, Evaldinólia Gilbertoni. "Dynamic coupling of multiscale land change models." Instituto Nacional de Pesquisas Espaciais, 2009. http://urlib.net/sid.inpe.br/mtc-m18@80/2008/11.10.16.23.

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Mudanças terrestres são resultados de uma complexa rede de interações entre fatores humanos e biofísicos, que atuam em diferentes escalas temporais e espaciais. Entender estes processos de mudanças terrestres de escalas locais a globais e seus impactos no sistema acoplado homem-natureza é um enorme desafio científico. Modelos em uma única escala podem não ser capazes capturar tais interações e processos de mudança. Esta tese apresenta uma metodologia para a construção de modelos de mudanças terrestre multiescala e multilocalidade, incluindo interações top-down e bottom-up. Numa primeira etapa, conceituamos dois tipos de relações espaciais entre objetos geográficos em diferentes escalas. Relações hierárquicas são propostas para tratar das interações entre objetos espacialmente aninhados, e relações de "ação à distância" são propostas para tratar de interações entre redes e objetos espaciais. Então, numa segunda etapa, apresentamos uma proposta de organização modular do software dos modelos. Consideramos neste trabalho o caso onde modelos para cada escala são independentemente construídos, possivelmente com abordagens distintas, e então dinamicamente acoplados. Conceitos de acopladores de modelos são introduzidos para definir o fluxo de informação bidirecional entre escalas. Estes conceitos foram implementado no ambiente de modelagem TerraME. Como prova de conceito, apresentamos um exemplo com duas escalas hierárquicas para Amazônia Brasileira. A conclusão deste trabalho aponta que a combinação de relações espaciais hierárquicas com relações baseadas em redes provê um arcabouço conceitual abrangente para lidar com interações top-down e bottom-up em modelos multiescala de mudanças terrestres. A organização modular e o conceito de acopladores propostos são bastante genéricos para serem usados com outros tipos de aplicação, e com isso contribuir para criação de modelos ambientais integrados, considerando escalas locais a globais.
Land changes are the result of a complex web of interactions between human and biophysical factors, which act over a wide range of temporal and spatial scales. Understanding processes of change from local to global scale and their impacts on the coupled human-environmental system is a main scientific challenge. No single model or scale can fully capture such interactions and processes of land change. This thesis presents a methodology for building multiscale, multi-locality land change models that include top-down and bottom-up relations. At first, we conceptualize two types of spatial relations among geographic objects at different scales. To handle the interaction of nested spatial objects at different scales, we propose hierarchical relations. To handle the interaction between networks and spatial objects, we propose action-at-a-distance relations. Then in a second step, we propose a modular software organization to build multiscale land change models. We consider the case when single-scale models, using different modeling approaches, are independently built and then dynamically coupled. We introduce the concepts of Model Couplers to define the bi-directional flow of information between the scales. We implement these concepts using the TerraME modeling environment. As a proof of concept, we present a hierarchical two-scale example for the Brazilian Amazon. The conclusion of this work points out that combining hierarchical and network-based spatial relations provides a comprehensive conceptual framework to include top-down and bottom-up interactions and feedbacks in multi-scale land-change models. The modular software organization and concept of Model Couplers are general enough to be used for other types of applications, and to contribute to the creation of Integrated Environmental Models from local to global scales.
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Zeileis, Achim, Friedrich Leisch, Christian Kleiber, and Kurt Hornik. "Monitoring structural change in dynamic econometric models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/1296/1/document.pdf.

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The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer better power against certain alternatives, improved size in finite samples for dynamic models and ease of computation respectively. We apply our methods to two data sets, German M1 money demand and U.S. labor productivity.
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Barth, Volker. "Integrated assessment of climate change using structural dynamic models." Hamburg : Max-Planck-Inst. für Meteorologie, 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=968535933.

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Wu, Ning. "Artificial intelligence solutions for models of dynamic land use change." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610781.

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Wiener, Karl Kilian Konrad, and n/a. "DYNAMIC CHANGE PROCESS: HOW DO COGNITIVE READINESS DRIVERS INFORM CHANGE AGENTS ON EMPLOYEE BEHAVIOURAL CHANGE INTENTION." University of Canberra. n/a, 2008. http://erl.canberra.edu.au./public/adt-AUC20081217.120215.

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It is well accepted by now that most change initiatives are unsuccessful even though more organisations are experiencing change as they fight to retain and improve their competitiveness in the market place. It is against this background of change failure that researchers have looked for new strategies to improve change outcomes. Theoretical models conceptualising the dynamic change process advise on better change strategies, but little empirical evidence has demonstrated that these models are effective in improving change implementation outcomes. Theoretical models were also developed to counter change resistance, but little emphasis has been placed on employee change readiness. Some empirical research on employee change readiness explores employees? perception of organisational readiness, but no empirical research has explored employee readiness from a psychological perspective. That is, how to create change readiness in employees. This thesis has contributed to both the theoretical and empirical understanding of the change readiness model. Firstly, the theoretical readiness for change model Armenakis et al.?s (1993, 2002) was extended by the inclusion of the ?understanding of the change? driver. Secondly, this change readiness model was empirically tested on two distinct organisational changes: organisational restructure and IT change. The extended model is also examined for two change stages of the dynamic process to identify which readiness drivers should be prioritised by change agents. Two online questionnaires were administered eight months apart assessing the responses to three change stages (planning, implementation and post-implementation) of employees ? supervisors and subordinates - of a flat structured organisation in the human resource industry. At the two measurement points 189 and 141 employees returned completed surveys. Six employee readiness drivers were operationalised and regressed against behavioural change intention. The quantitative findings using regression models across two change types and longitudinally did not identify a specific change pattern. However, all six readiness drivers including the ?understanding of the change? driver were influential on employees? behavioural change intention. Furthermore, statistical differences between supervisors and subordinates were identified in the organisational restructure change. The quantitative findings using a triangulation approach with qualitative date including data from two unstructured interviews and employee comments further validated the quantitative findings. The thematic analysis of the employee comments enhanced the findings and identified employee specific concerns including information dissemination of the changes and a level of uncertainty. The findings supported Armenakis et al.?s (1993, 2002) theoretical contribution that change readiness drivers are an important part of the organisational change process explaining why employee do and do not change. The empirical application of readiness change driver evaluation during the dynamic change is supported as it permits change agents to directly monitor employees? readiness perception of a specific change target. This valuable information finds practical utilisation for change agents in providing targeted guidance and support for employees thus facilitating a greater likelihood of a positive change outcome. Implications of these findings and future research opportunities are discussed.
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Zurell, Damaris. "Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5684/.

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Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species’ distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this ‘virtual ecologist’ approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species’ response to altered environmental conditions and which should hence be considered when trying to project species’ distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species’ response to environmental change, identify key challenges for future research and discuss emerging developments.
Das Vorkommen von Arten wird zunehmend bedroht durch Klima- und Landnutzungswandel. Robuste Vorhersagen der damit verbundenen Arealveränderungen sind ausschlaggebend für die Erarbeitung dynamischer und nachhaltiger Naturschutzstrategien. Habitateignungsmodelle erstellen statistische Zusammenhänge zwischen dem Vorkommen einer Art und relevanten Umweltvariablen und erlauben zügige Einschätzungen potentieller Arealveränderungen. Dabei werden jedoch transiente Dynamiken weitgehend ignoriert sowie demographische Prozesse und biotische Interaktionen. Daher wurden Vorschläge laut, diese statistischen Modelle mit mechanistischeren Ansätzen zu koppeln. In der vorliegenden Arbeit zeige ich zwei verschiedene Möglichkeiten solcher Kopplung auf. (i) Ich beschreibe den sogenannten ‚Virtuellen Ökologen’-Ansatz als mächtiges Validierungswerkzeug, in dem mechanistische Modelle virtuelle Testflächen bieten zur Erforschung verschiedener Probenahmedesigns oder statistischer Methoden sowie spezifischer Fragestellungen. Auch verwende ich diesen Ansatz, um systematisch zu untersuchen wie sich transiente Dynamiken sowie Arteigenschaften und ökologische Prozesse auf die Vorhersagegüte von Habitateignungsmodellen auswirken. So kann ich entscheidende Prozesse identifizieren welche in zukünftigen Modellen Berücksichtigung finden sollten. (ii) Darauf aufbauend koppele ich Vorhersagen von Habitateignungsmodellen mit einem individuen-basierten Populationsmodell, um die Entwicklung des Schweizer Birkhuhnbestandes unter Klimawandel vorherzusagen. Durch die explizite Berücksichtigung der Wechselwirkungen zwischen Habitat und demographischer Prozesse lassen sich direktere Aussagen über Populationsentwicklung und damit verbundener Extinktionsrisiken treffen. Allerdings führen verschiedene Simulationen auch zu hoher Variabilität zwischen Vorhersagen, was die Bedeutung von Sensitivitätsanalysen unterstreicht, um Unsicherheiten und Robustheit von Vorhersagen einzuschätzen. Außerdem identifiziere ich Restriktionen in der Datenabdeckung des Umweltraumes als möglichen Grund für kontrastierende Vorhersagen verschiedener Habitateignungsmodelle. Wenn die Nische einer Art nicht vollständig durch Daten beschrieben ist, kann dies zu unrealistischen Vorhersagen der Art-Habitat-Beziehung führen. Insgesamt trägt meine Arbeit erheblich bei zu einem besseren Verständnis der Auswirkung verschiedenster Unsicherheitsfaktoren auf Vorhersagen von Arealveränderungen und zeigt Wege auf, mit diesen umzugehen. Abschließend erstelle ich einen vorläufigen Leitfaden für Vorhersagemodelle und identifiziere Kernpunkte für weitere Forschung auf diesem Gebiet.
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Barth, Volker [Verfasser]. "Integrated assessment of climate change using structural dynamic models / Max-Planck-Institut für Meteorologie. Von Volker Barth." Hamburg : Max-Planck-Inst. für Meteorologie, 2003. http://d-nb.info/968535933/34.

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Davis, Michael C. "Dynamic models of price changes /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2001. http://wwwlib.umi.com/cr/ucsd/fullcit?p3026374.

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Kasun, Matthew R. (Matthew Roger) Carleton University Dissertation Engineering Systems and computer. "A meta-model for dynamic change management." Ottawa, 1993.

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Juntunen, M. (Marko). "Business model change as a dynamic capability." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526216621.

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Abstract The purpose of this study is to explore the role of dynamic capability in business model change in Internet-based business start-ups and Internet-based business enterprises. This study reviews the literature on business models, business model change, and dynamic capability, and defines business model change as a dynamic capability that is expected to lead to increased speed of business change and competitive advantages over the long term. This empirical study of business model change is accomplished by the case study method. The study is based on field data of four case companies regarding entrepreneurs’, business developers’, and business leaders’ behavior and actions while they are utilizing the business model concept for the purpose of business change. This study approaches business model change as a dynamic capability from three dynamic capability cluster perspectives. It explores how businesses can use the concept of business model for business decisions as well as how they can use the concept of business model to improve the speed of business change after new business opportunities or threats are found. The analysis identifies the process of business model change and factors that affect business model change, and the outcomes are results from three dynamic capability clusters that Internet-based companies are able to follow in a fast-changing business environment. This study develops a posteriori model proposing that a dynamically capable business model change consists of sensing, seizing, and transforming activities to obtain successful business and competitive advantages over the long term. A posteriori model of business model change as a dynamic capability creates a framework to support a quick business model change, especially in a fast-changing business environment. This study adds a dynamic capability viewpoint to the business model literature pertaining to business modeling and business model change. Regarding the managerial implications, this study shows how entrepreneurs and business owners can utilize the concept of business model in order to support a quick business change and possibly gain a competitive advantage in the long term. The study results indicate that internal and external factors for business model change are company-specific and those vary between the start-up and enterprise. And thus, a successful business model change can be achieved through analyzing and further developing these company-specific factors. These factors can be seen as a micro foundation of dynamic capability, and development of these factors can improve competitive advantage
Tiivistelmä Tässä tutkimuksessa tarkastellaan nuoreen Internet liiketoimintaan pohjautuvaan kasvuyritykseen ja kypsiin Internet liiketoimintaan pohjautuviin yrityksiin tehtyjä liiketoimintamallimuutoksia dynaamisten kyvykkyyksien näkökulmasta. Tutkimuksen aihetta lähestytään nuoren kasvuyrityksen sekä kypsien yritysten kannalta ja työn teoreettinen viitekehys rakennetaan liiketoimintamallin, liiketoimintamallimuutoksen sekä dynaamisen kyvykkyyden tutkimusjulkaisujen kautta. Työssä liiketoimintamallimuutos määritellään dynaamiseksi kyvykkyydeksi joka kehittyy eri toimintojen avulla. Tämä dynaaminen kyvykkyys voi mahdollistaa nopeamman liiketoimintamuutoksen ja pitkällä aikavälillä se voi vaikuttaa myös kilpailuedun saavuttamiseen. Työn empiirisessä osassa liiketoimintamallimuutosta tutkitaan tapaustutkimusmenetelmällä neljässä yrityksessä. Yrittäjien, liiketoimintakehittäjien, sekä johtajien käyttäytymistä ja toimenpiteitä tutkitaan liiketoimintamallikonseptin kautta muutostilanteessa. Yrittäjien, liiketoimintakehittäjien, sekä johtajien käyttäytymistä ja toimenpiteitä tutkitaan liiketoimintamallikonseptin kautta muutostilanteessa. Työssä dynaamista liiketoimintamallimuutosta lähestytään kolmen dynaamiselle kyvykkyydelle määritellyn klusterin kautta (havaitseminen, tarttuminen, sekä uhkien hallinta ja muuntautuminen). Nämä klusterit toimivat erityisesti silloin kun yritysten tulee tehdä nopeita liiketoimintapäätöksiä ja liiketoimintamuutoksia. Analyysissa tunnistetaan liiketoimintamallin muutosprosessi, sekä sisäiset ja ulkoiset tekijät joilla on vaikutusta liiketoimintamallimuutokseen. Lopputulokset ovat seuraus kolmesta dynaamisen ominaisuuden klusterista joita organisaatioiden tulisi huomioida nopeasti muuttuvassa ympäristössä. Tutkimuksessa kehitetään jälkikäteismalli jossa liiketoimintamallimuutos dynaamisena kyvykkyytenä synnytetään dynaamisen kyvykkyyden klustereissa esiintyvien toimenpiteiden kautta. Näiden toimenpiteiden avulla on mahdollista saavuttaa pitkällä aikavälillä menestystä ja mahdollisesti myös kilpailuetua. Jälkikäteismalli tukee dynaamista liiketoimintamallimuutosta nopeasti muuttuvassa liiketoimintaympäristössä. Työn teoreettinen kontribuutio on erityisesti dynaamisen kyvykkyyden näkökulma liiketoimintamallintamiseen ja liiketoimintamallimuutokseen. Työssä osoitetaan myös kuinka yrittäjät ja liiketoimintaomistajat voivat käytännössä hyödyntää liiketoimintamallikonseptia nopeasti tehtävään liiketoimintamuutokseen. Tutkimustulokset osoittavat, että sisäisillä ja ulkoisilla tekijöillä on vaikutusta liiketoimintamallimuutokseen. Nämä tekijät ovat yrityskohtaisia, ja nuoren kasvuyrityksen ja kypsien yritysten väliltä löytyy eroja. Sen vuoksi onnistunut liiketoimintamallimuutos voidaan tehdä näitä tekijöitä analysoimalla ja kehittämällä
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Books on the topic "Dynamic change models"

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Jeffers, J. N. R. Practitioners handbook on the modelling of dynamic change in ecosystems. Chichester: Published on behalf of the Scientific Committee on Problems of the Environment (SCOPE) of the International Council of Scientific Unions (ICSU) by Wiley, 1988.

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Gemmell, Norman. Dynamic sectoral linkages and structural change in a developing economy. Nottingham: University of Nottingham, Centre for Research in Economic Development and International Trade, 1998.

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Geert, Paul van. Dynamic systems of development: Change between complexity and chaos. New York: Harvester Wheatsheaf, 1994.

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Jeffers, J. N. R. Practitioner's handbook on the modelling of dynamic change in ecosystems. Chichester [West Sussex]: Published on behalf of the Scientific Committee on Problems of the Environment of the International Council of Scientific Unions by Wiley, 1988.

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Aulin-Ahmavaara, Pirkko. A dynamic input-output model with non-homogeneous labour for evaluation of technical change. Helsinki: Suomalainen Tiedeakatemia, 1987.

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Becker, Torbjörn. Common trends and structural change: A dynamic macro model for the pre- and postrevolution Islamic Republic of Iran. [Washington, D.C.]: International Monetary Fund, Research Department, 1999.

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Canada. Minerals and Metals Sector. Economic impact of carbon abatement policies and market structure: A dynamic general equilibrium analysis with imperfect competition. Ottawa: Industry Canada, 2001.

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Ulrike, Jessner, ed. A dynamic model of multilingualism: Perspectives of change in psycholinguistics. Clevedon, England: Multilingual Matters, 2002.

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Baily, Martin Neil. Labor productivity: Structural change and cyclical dynamics. Cambridge, MA: National Bureau of Economic Research, 1996.

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Hlinka, Anton. Každý sa môže zmeniť: Dynamické modely spravania. Bratislava: Don Bosco, 1994.

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Book chapters on the topic "Dynamic change models"

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Gómez M., Guillermo L. "Change and time in development and growth processes." In Dynamic Probabilistic Models and Social Structure, 117–69. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2524-6_3.

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Bahn, Olivier, Alain Haurie, and Roland Malhamé. "Limit Game Models for Climate Change Negotiations." In Advances in Dynamic and Mean Field Games, 27–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70619-1_2.

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Perz, Stephen, Joseph P. Messina, Eustaquio Reis, Robert Walker, and Stephen J. Walsh. "Scenarios of future Amazonian landscapes: Econometric and dynamic simulation models." In Amazonia and Global Change, 83–100. Washington, D. C.: American Geophysical Union, 2009. http://dx.doi.org/10.1029/2008gm000736.

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Bose, Prasanta, and Mark G. Matthews. "An Agent Mediated Approach to Dynamic Change in Coordination Policies." In Coordination Languages and Models, 164–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45263-x_11.

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Horincar, Roxana, Bernd Amann, and Thierry Artières. "Online Change Estimation Models for Dynamic Web Resources." In Lecture Notes in Computer Science, 395–410. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31753-8_33.

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Rangelova, E., W. van der Wal, M. G. Sideris, and P. Wu. "Numerical models of the rates of change of the geoid and orthometric heights over Canada." In Dynamic Planet, 563–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-49350-1_82.

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Dutta, Prajit K. "Dynamic Games with an Application to Climate Change Models." In Complex Social and Behavioral Systems, 61–82. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0368-0_137.

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Dutta, Prajit K. "Dynamic Games with an Application to Climate Change Models." In Encyclopedia of Complexity and Systems Science, 1–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-642-27737-5_137-2.

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Wu, Jianguo, G. Darrel Jenerette, and John L. David. "Linking Land-use Change with Ecosystem Processes: A Hierarchical Patch Dynamic Model." In Integrated Land Use and Environmental Models, 99–119. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05109-2_5.

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Andrey, Christopher, Olivier Bahn, and Alain Haurie. "Computing α-Robust Equilibria in Two Integrated Assessment Models for Climate Change." In Advances in Dynamic and Evolutionary Games, 283–300. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28014-1_14.

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Conference papers on the topic "Dynamic change models"

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Aitio, Antti, and David Howey. "Combining Non-Parametric and Parametric Models for Stable and Computationally Efficient Battery Health Estimation." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3180.

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Abstract Equivalent circuit models for batteries are commonly used in electric vehicle battery management systems to estimate state of charge and other important latent variables. They are computationally inexpensive, but suffer from a loss of accuracy over the full range of conditions that may be experienced in real-life. One reason for this is that the model parameters, such as internal resistance, change over the lifetime of the battery due to degradation. However, estimating long term changes is challenging, because parameters also change with state of charge and other variables. To address this, we modelled the internal resistance parameter as a function of state of charge and degradation using a Gaussian process (GP). This was performed computationally efficiently using an algorithm [1] that interprets a GP to be the solution of a linear time-invariant stochastic differential equation. As a result, inference of the posterior distribution of the GP scales as 𝒪(n) and can be implemented recursively using a Kalman filter.
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Wang, Yu, Aniket Chakrabarti, David Sivakoff, and Srinivasan Parthasarathy. "Fast Change Point Detection on Dynamic Social Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/417.

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A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.
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van der Aalst, W. M. P. "Generic workflow models: how to handle dynamic change and capture management information?" In Proceedings Fourth IFCIS International Conference on Cooperative Information Systems. CoopIS 99 (Cat. No.PR00384). IEEE, 1999. http://dx.doi.org/10.1109/coopis.1999.792163.

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Eger, Steffen, and Alexander Mehler. "On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-2009.

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Luo, Ping, Xue Cao, Hongga Li, and Manchun Li. "Dynamic simulation of land use change in shenzhen city based on Markov-logistic-CA models." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567756.

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Yi, Wei, Zhiqiang Gao, and Maosi Chen. "Dynamic modelling of future land-use change: a comparison between CLUE-S and Dinamica EGO models." In SPIE Optical Engineering + Applications, edited by Wei Gao, Thomas J. Jackson, Jinnian Wang, and Ni-Bin Chang. SPIE, 2012. http://dx.doi.org/10.1117/12.927781.

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Yepifanov, Sergiy V., Roman L. Zelenskyi, and Igor Loboda. "Modeling the GTE Under Its Dynamic Heating Conditions." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-26258.

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A modern gas turbine engine (GTE) is a complex non-linear dynamic system with the mutual effect of gas-dynamic and thermal processes in its components. The engine development requires the precise real-time simulation of all main operating modes. One of the most complex operating modes for modeling is “cold stabilization”, which is the rotors acceleration without completely heated up the turbine elements. The dynamic heating problem is a topical practical issue. Solving the problem requires coordinating a gas-path model with heat and stress models, which is also a significant scientific problem. The phenomenon of interest is the radial clearances change during engines operation and its influence on engines static and dynamic performances. To consider the clearance change, it is necessary to synthesize the quick proceeding stress-state models (QPSSM) of a rotor and a casing for the initial temperature and dynamic heating. The unique feature of the QPSSM of GTEs is separate equation sets, which allow the heat exchange between structure elements and the gas (air) and the displacements of the turbine rotor and the casing. This ability appears as a result of determining the effect of each factor on different structural elements of the engine. The presented method significantly simplifies the model identification, which can be performed based on a precise calculation of the unsteady temperature fields of the structural elements and the variation of the radial clearance. Thus, the present paper addresses a new method to model the engine dynamics considering its heating up. The method is based on the integration of three models: the gas-path dynamics model, the clearance dynamics model and the model of the clearance effect on the efficiency. The paper also comprises the program implementation of the models. The method was tested by applying to a particular turbofan engine.
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Cantero, Katty M., and Marco E. Sanjuan. "Dynamic Model Fuzzy Transition in DMC Type Controllers for Varying Dynamics in Bioreactors." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67471.

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This research presents an approach to implement industrial type DMC (Dynamic Matrix Control) controllers in biological process where a fed-batch operation is required. In this type of operation a substantial change in the process dynamics occurs due to changes in volume, reaction rate, pressure, temperature, concentration, and other process variables. To compensate for changes in the dynamic, several matrix models are identified, and using a fuzzy supervisor a soft model transition is implemented, such that the controller and the process performance can adapt to the new operating conditions without affecting the process integrity. The control system design, its identification, and implementation are presented, focusing in the articulation between MPC (Model Predictive Control) and Fuzzy Supervision. The overall strategy is demonstrated at a simulation level using a benchmark fermentation process for Penicillin production in Fed-Batch mode. In this process glucose is used as a substrate, and a fungus, penicillium chrysogenum, is used in a fed-batch reactor to obtain penicillin. The model presents an unstructured approach to the reactor dynamic system, and the initial steps for this research required the development of an identification procedure for such zones.
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Kim, Eunkyeong, Takuya Yoshida, and Tatsurou Yashiki. "Dynamic Heat-Exchanger Model for Any Combination of Water and Steam States." In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icone20-power2012-54203.

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The purpose of this study is to propose a dynamic heat transfer model for predicting transient heat recovery steam generator (HRSG) behaviors involving phase changes in heat exchanger tubes. The model deals with any combination of phase states by switching the equations for heat transfer coefficient, specific volume, and friction factor corresponding to their physical characteristics. The model also constrains the change of mass flow calculated by momentum balance to satisfy thermodynamic relationships which are neglected by conventional models. The simulation results show that the proposed model predicts the transient pressure drop, outlet mass flow changes and the reduction in heat transfer coefficient caused by dryout during heating or evaporating processes. In addition, the model improves the accuracy of mass flow transients compared to those obtained by conventional models.
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Skartveit, Hanne-Lovise, Katherine J. Goodnow, and Magnhild Viste. "Visualized System Dynamics models as Information and Planning Tools." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2693.

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In this paper, we describe the use of visualization of system dynamics models as client information and management tools. System dynamics is a methodology for analyzing and understanding how complex systems change over time. System dynamic models have been developed for a broad range of information to client applications - from resource management problems to the mapping of stocks and flows on factory floors. The problem faced by many users of system dynamic models is their graphic complexity for users not trained in the field. This paper addresses new research into visualization of system dynamics models to make client information more efficient and accessible. This research involves the use of narrative, video and sound embedded in statistical material. This paper also considers one particular client group - that of politicians, planners and civil society in developing countries.
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Reports on the topic "Dynamic change models"

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Li, Jing. Various New Statistical Models for Modeling and Change Detection in Multidimensional Dynamic Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada606729.

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Guldi, Jo. Parliament's Debates about Infrastructure: An Exercise in Using Dynamic Topic Models to Synthesize Historical Change (annotated version). Roy Rosenzweig Center for History and New Media, 2021. http://dx.doi.org/10.31835/ma.2021.08.

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Verburg, Peter H., Žiga Malek, Sean P. Goodwin, and Cecilia Zagaria. The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003625.

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The Conversion of Land Use and its Effects modeling framework (CLUE) was developed to simulate land use change using empirically quantified relations between land use and its driving factors in combination with dynamic modeling of competition between land use types. Being one of the most widely used spatial land use models, CLUE has been applied all over the world on different scales. In this document, we demonstrate how the model can be used to develop a multi-regional application. This means, that instead of developing numerous individual models, the user only prepares one CLUE model application, which then allocates land use change across different regions. This facilitates integration with the Integrated Economic-Environmental Modeling (IEEM) Platform for subnational assessments and increases the efficiency of the IEEM and Ecosystem Services Modeling (IEEMESM) workflow. Multi-regional modelling is particularly useful in larger and diverse countries, where we can expect different spatial distributions in land use changes in different regions: regions of different levels of achieved socio-economic development, regions with different topographies (flat vs. mountainous), or different climatic regions (dry vs humid) within a same country. Accounting for such regional differences also facilitates developing ecosystem services models that consider region specific biophysical characteristics. This manual, and the data that is provided with it, demonstrates multi-regional land use change modeling using the country of Colombia as an example. The user will learn how to prepare the data for the model application, and how the multi-regional run differs from a single-region simulation.
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Niyogi, Partha, and Robert C. Berwick. A Dynamical Systems Model for Language Change. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada307027.

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.
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Agarwal, Chetan, Glen M. Green, J. Morgan Grove, Tom P. Evans, and Charles M. Schweik. A review and assessment of land-use change models: dynamics of space, time, and human choice. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 2002. http://dx.doi.org/10.2737/ne-gtr-297.

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Robertson, A. W., M. Ghil, K. Kravtsov, and P. J. Smyth. Final Technical Report for "Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models". Office of Scientific and Technical Information (OSTI), April 2011. http://dx.doi.org/10.2172/1010911.

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Kravtsov, S., Andrew W. Robertson, Michael Ghil, and Padhraic J. Smyth. Final Technical Report for "Collaborative Research. Regional climate-change projections through next-generation empirical and dynamical models". Office of Scientific and Technical Information (OSTI), April 2011. http://dx.doi.org/10.2172/1010914.

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Ziegler, Nancy, Nicholas Webb, Adrian Chappell, and Sandra LeGrand. Scale invariance of albedo-based wind friction velocity. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40499.

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Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity (u*) derived from traditional methods (wind speed profiles) with those derived from the albedo model at two separate scales using bare soil patch (via net radiometers) and landscape (via MODIS 500 m) datasets. Results show that profile-derived estimates of u* are highly variable in anisotropic surface roughness due to changes in wind direction and fetch. Wind speed profiles poorly estimate soil surface (bed) wind friction velocities necessary for aeolian sediment transport research and modeling. Albedo-based estimates of u* at both scales have small variability because the estimate is integrated over a defined, fixed area and resolves the partition of wind momentum be-tween roughness elements and the soil surface. We demonstrate that the wind tunnel-based calibration of albedo for predicting wind friction velocities at the soil surface (us*) is applicable across scales. The albedo-based approach enables consistent and reliable drag partition correction across scales for model and field estimates of us* necessary for wind erosion and dust emission modeling.
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Smyth, Padhraic. Final Technical Report for Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models, DE-FG02-07ER64429. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1088032.

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